US11630409B2 - Image processing method, image processing apparatus - Google Patents
Image processing method, image processing apparatus Download PDFInfo
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- US11630409B2 US11630409B2 US17/558,338 US202117558338A US11630409B2 US 11630409 B2 US11630409 B2 US 11630409B2 US 202117558338 A US202117558338 A US 202117558338A US 11630409 B2 US11630409 B2 US 11630409B2
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03G—ELECTROGRAPHY; ELECTROPHOTOGRAPHY; MAGNETOGRAPHY
- G03G15/00—Apparatus for electrographic processes using a charge pattern
- G03G15/50—Machine control of apparatus for electrographic processes using a charge pattern, e.g. regulating differents parts of the machine, multimode copiers, microprocessor control
- G03G15/5062—Machine control of apparatus for electrographic processes using a charge pattern, e.g. regulating differents parts of the machine, multimode copiers, microprocessor control by measuring the characteristics of an image on the copy material
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03G—ELECTROGRAPHY; ELECTROPHOTOGRAPHY; MAGNETOGRAPHY
- G03G15/00—Apparatus for electrographic processes using a charge pattern
- G03G15/50—Machine control of apparatus for electrographic processes using a charge pattern, e.g. regulating differents parts of the machine, multimode copiers, microprocessor control
- G03G15/5054—Machine control of apparatus for electrographic processes using a charge pattern, e.g. regulating differents parts of the machine, multimode copiers, microprocessor control by measuring the characteristics of an intermediate image carrying member or the characteristics of an image on an intermediate image carrying member, e.g. intermediate transfer belt or drum, conveyor belt
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03G—ELECTROGRAPHY; ELECTROPHOTOGRAPHY; MAGNETOGRAPHY
- G03G15/00—Apparatus for electrographic processes using a charge pattern
- G03G15/50—Machine control of apparatus for electrographic processes using a charge pattern, e.g. regulating differents parts of the machine, multimode copiers, microprocessor control
- G03G15/5075—Remote control machines, e.g. by a host
- G03G15/5087—Remote control machines, e.g. by a host for receiving image data
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03G—ELECTROGRAPHY; ELECTROPHOTOGRAPHY; MAGNETOGRAPHY
- G03G15/00—Apparatus for electrographic processes using a charge pattern
- G03G15/55—Self-diagnostics; Malfunction or lifetime display
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/00002—Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for
- H04N1/00026—Methods therefor
- H04N1/00039—Analysis, i.e. separating and studying components of a greater whole
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/00002—Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for
- H04N1/00071—Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for characterised by the action taken
- H04N1/00074—Indicating or reporting
- H04N1/00076—Indicating or reporting locally
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/46—Colour picture communication systems
- H04N1/56—Processing of colour picture signals
- H04N1/58—Edge or detail enhancement; Noise or error suppression, e.g. colour misregistration correction
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03G—ELECTROGRAPHY; ELECTROPHOTOGRAPHY; MAGNETOGRAPHY
- G03G2215/00—Apparatus for electrophotographic processes
- G03G2215/00025—Machine control, e.g. regulating different parts of the machine
- G03G2215/00029—Image density detection
- G03G2215/00033—Image density detection on recording member
- G03G2215/00037—Toner image detection
- G03G2215/00042—Optical detection
Definitions
- the present disclosure relates to an image processing method and an image processing apparatus for determining whether or not a noise point in an image is a sheet noise.
- An image forming apparatus such as a printer or a multifunction peripheral executes a print process to form an image on a sheet.
- a print process an image defect such as a vertical stripe, a horizontal stripe, a noise point, or density variation may be generated on the image formed on an output sheet.
- the image defect may be caused by any of various parts such as a photoconductor, a charging portion, a developing portion, and a transfer portion.
- a photoconductor for example, a photoconductor, a charging portion, a developing portion, and a transfer portion.
- an image processing apparatus that preliminarily stores, as table data, correspondence between: phenomena that cause the vertical stripe as an example of the image defect; and feature information such as the color of the vertical stripe, density, and the number of screen lines, wherein a phenomenon that has caused the vertical stripe is identified based on information of the color of an image of the vertical stripe, density, or the number of screen lines in a test image and the table data.
- An image processing method includes a processor selecting a target sheet from a plurality of predetermined sheet candidates in accordance with selection information that is input via an input device. Furthermore, the image processing method includes the processor deriving feature information regarding a noise point from a target image that is obtained through an image reading process performed on an output sheet output from an image forming device, the noise point being a dot-like noise image included in the target image.
- the image processing method includes the processor executing a sheet noise determination process to determine whether or not the noise point is a dot-like sheet noise by applying the feature information to a determination algorithm that corresponds to the target sheet, the sheet noise being included in a sheet of the output sheet itself, the determination algorithm being one of a plurality of determination algorithms that respectively correspond to the plurality of sheet candidates.
- An image processing apparatus includes the processor that executes processes of the image processing method.
- FIG. 1 is a configuration diagram of an image processing apparatus according to an embodiment.
- FIG. 2 is a block diagram showing a configuration of a data processing portion in the image processing apparatus according to the embodiment.
- FIG. 3 is a flowchart showing an example of a procedure of an image defect determination process in the image processing apparatus according to the embodiment.
- FIG. 4 is a flowchart showing an example of a procedure of a specific defect determination process in the image processing apparatus according to the embodiment.
- FIG. 5 is a flowchart showing an example of a procedure of a sheet noise removal process in the image processing apparatus according to the embodiment.
- FIG. 6 is a diagram showing an example of a test image including specific parts and examples of pre-process images and feature images generated based on the test image.
- FIG. 7 is a diagram showing an example of a focused area and adjacent areas that are sequentially selected from the test image in a main filter process executed by the image processing apparatus according to the embodiment.
- FIG. 8 A to FIG. 8 C each show an example of a noise point, a pixel value distribution of a transverse pixel sequence, and a differential value distribution of the transverse pixel sequence.
- FIG. 8 A shows a case where the symptom of the noise point is mild.
- FIG. 8 B shows a case where the symptom of the noise point is middle.
- FIG. 8 C shows a case where the symptom of the noise point is serious.
- FIG. 9 is a flowchart showing an example of a procedure of a feature image generating process in a first application example of the image processing apparatus according to the embodiment.
- an image processing apparatus 10 includes an image forming device 2 that executes a print process.
- an image is formed on a sheet.
- the sheet is an image formation medium such as a sheet of paper or a sheet-like resin material.
- the image processing apparatus 10 includes an image reading device 1 that executes a reading process to read an image from a document sheet.
- the image processing apparatus 10 is a copier, a facsimile apparatus, or a multifunction peripheral.
- the image targeted to be processed in the print process is, for example, an image read from the document sheet by the image reading device 1 or an image represented by print data received from a host apparatus (not shown).
- the host apparatus is an information processing apparatus such as a personal computer or a mobile information terminal.
- the image forming device 2 may form a predetermined original test image g 01 on a sheet (see FIG. 6 ).
- the original test image g 01 is an original of a test image g 1 that is used to determine whether or not an image defect has been generated by the image forming device 2 and to determine the cause of the image defect (see FIG. 6 ).
- the test image g 1 is described below.
- a copy process includes: the reading process performed by the image reading device 1 ; and the print process performed by the image forming device 2 based on an image obtained in the reading process.
- the image forming device 2 includes a sheet conveying mechanism 3 and a print portion 4 .
- the sheet conveying mechanism 3 includes a sheet feed-out mechanism 31 and a plurality of pairs of sheet conveying rollers 32 .
- the sheet feed-out mechanism 31 feeds out a sheet from a sheet storage portion 21 to a sheet conveyance path 30 .
- the plurality of pairs of sheet conveying rollers 32 convey the sheet along the sheet conveyance path 30 , and discharge the sheet with an image formed thereon to a discharge tray 22 .
- the print portion 4 executes the print process on the sheet conveyed by the sheet conveying mechanism 3 .
- the print portion 4 executes the print process by an electrophotographic method.
- the print portion 4 includes an image creating portion 4 x , a bias output circuit 430 , a laser scanning unit 4 y , a transfer device 44 , and a fixing device 46 .
- the image creating portion 4 x includes a drum-like photoconductor 41 , a charging device 42 , a developing device 43 , and a drum cleaning device 45 .
- the photoconductor 41 rotates, and the charging device 42 electrically charges the surface of the photoconductor 41 uniformly.
- the charging device 42 includes a charging roller 42 a that rotates while in contact with the surface of the photoconductor 41 .
- the charging device 42 electrically charges the surface of the photoconductor 41 by outputting a charging voltage to the photoconductor 41 through the charging roller 42 a.
- the laser scanning unit 4 y writes an electrostatic latent image on the charged surface of the photoconductor 41 by scanning a laser light.
- the developing device 43 develops the electrostatic latent image as a toner image.
- the photoconductor 41 is an example of an image carrier that carries the toner image.
- the developing device 43 includes a developing roller 43 a , a developer tank 43 b , and a regulation blade 43 c .
- the developer tank 43 b stores toner.
- the developing roller 43 a supplies the toner to the surface of the photoconductor 41 by rotating while carrying the toner in the developer tank 43 b .
- the bias output circuit 430 applies a developing bias to each of the developing rollers 43 a .
- the reference potential of the developing bias is the potential of the photoconductor 41 .
- the bias output circuit 430 is configured to correct the developing bias.
- the regulation blade is configured to regulate the thickness of a layer of the toner carried by the developing roller 43 a . It is noted that four developing devices 43 corresponding to four types of developing colors are each an example of a developing portion.
- the image forming device 2 shown in FIG. 1 is a tandem-type color printer that is configured to execute the print process to process a color image.
- the print portion 4 includes four image creating portions 4 x corresponding to four different colors of toner.
- the four image creating portions 4 x have different developing colors.
- the transfer device 44 includes four primary transfer rollers 441 , an intermediate transfer belt 440 , a secondary transfer roller 442 , and a belt cleaning device 443 , wherein the four primary transfer rollers 441 correspond to four photoconductors 41 .
- the four image creating portions 4 x respectively form cyan, magenta, yellow, and black toner images on the surfaces of the photoconductors 41 .
- Each of the primary transfer rollers 441 is a part of a corresponding one of the image creating portions 4 x.
- the drum cleaning device 45 removes and collects, from the photoconductor 41 , toner that has remained on the photoconductor 41 without being transferred to the intermediate transfer belt 440 .
- the secondary transfer roller 442 transfers the toner images of the four colors from the intermediate transfer belt 440 to a sheet. It is noted that in the image processing apparatus 10 , the intermediate transfer belt 440 is an example of a transfer body that transfers the toner images to the sheet.
- the belt cleaning device 443 removes and collects, from the intermediate transfer belt 440 , toner that has remained on the intermediate transfer belt 440 without being transferred to the sheet.
- the data processing portion 8 executes various types of data processing concerning the print process and the reading process, and further controls various types of electric devices.
- the operation portion 801 is an example of an input device configured to input information in accordance with a user operation.
- the operation portion 801 includes either or both of a pushbutton and a touch panel.
- the display portion 802 includes a display panel that displays information for the users.
- the data processing portion 8 includes a CPU (Central Processing Unit) 80 , a RAM (Random Access Memory) 81 , a secondary storage device 82 , and a communication device 83 .
- a CPU Central Processing Unit
- RAM Random Access Memory
- the CPU 80 is configured to process data received by the communication device 83 , and perform controls of various types of image processing and the image forming device 2 .
- the received data may include print data.
- the CPU 80 is an example of a processor that executes data processing including the image processing. It is noted that the CPU 80 may be realized by another type of processor such as a DSP (Digital Signal Processor).
- DSP Digital Signal Processor
- the communication device 83 is a communication interface device that performs communication with other apparatuses such as the host apparatus via a network such as a LAN (Local Area Network).
- the CPU 80 performs data transmissions and receptions with the external apparatuses all via the communication device 83 .
- the secondary storage device 82 is a computer-readable nonvolatile storage device.
- the secondary storage device 82 stores computer programs executed by the CPU 80 and various types of data referenced by the CPU 80 .
- a flash memory and a hard disk drive are adopted as the secondary storage device 82 .
- the RAM 81 is a computer-readable volatile storage device.
- the RAM 81 primarily stores the computer programs executed by the CPU 80 and data that is output and referenced by the CPU 80 during execution of the programs.
- the CPU 80 includes a plurality of processing modules that are realized when the computer programs are executed.
- the plurality of processing modules include a main control portion 8 a and a job control portion 8 b . It is noted that a part or all of the plurality of processing modules may be realized by another type of processor such as the DSP that is independent of the CPU 80 .
- the main control portion 8 a executes a process to select a job in response to an operation performed on the operation portion 801 , a process to display information on the display portion 802 , and a process to set various types of data. Furthermore, the main control portion 8 a executes a process to determine the content of data received by the communication device 83 .
- the job control portion 8 b controls the image reading device 1 and the image forming device 2 .
- the job control portion 8 b causes the image forming device 2 to execute the print process.
- the job control portion 8 b causes the image reading device 1 to execute the reading process and causes the image forming device 2 to execute the print process based on an image obtained in the reading process.
- a noise image such as a vertical stripe Ps 11 , a horizontal stripe Ps 12 , or a noise point Ps 13 may be generated on an image formed on an output sheet (see FIG. 6 ).
- the vertical stripe Ps 11 is a line-like noise image extending in the sub scanning direction D 2 .
- the horizontal stripe Ps 12 is a line-like noise image extending in the main scanning direction D 1 .
- the noise point Ps 13 is a dot-like noise image.
- the image forming device 2 executes the print process by the electrophotographic method.
- the noise image may be caused by any of various parts such as the photoconductor 41 , the charging device 42 , the developing device 43 , and the transfer device 44 .
- it requires skill to determine the cause of the noise image.
- the image forming device 2 executes a test print process to form a predetermined original test image g 01 on a sheet (see FIG. 6 ).
- the job control portion 8 b causes the image forming device 2 to execute the test print process.
- the sheet on which the original test image g 01 has been formed is referred to as a test output sheet 9 (see FIG. 1 ).
- the main control portion 8 a displays a predetermined guide message on the display portion 802 .
- the guide message urges setting the test output sheet 9 on the image reading device 1 and then performing a reading start operation on the operation portion 801 .
- the job control portion 8 b causes the image reading device 1 to execute the reading process. This allows the original test image g 01 to be read by the image reading device 1 from the test output sheet 9 output from the image forming device 2 , and a read image corresponding to the original test image g 01 is obtained.
- the CPU 80 determines the cause of the noise image.
- the test image g 1 is the read image or a compressed image of the read image.
- the CPU 80 is an example of a processor that executes a process of an image processing method to determine the cause of the noise image.
- the original test image g 01 may be read from the test output sheet 9 by a device such as a digital camera. It is noted that a process in which the image reading device 1 or the digital camera reads the original test image g 01 from the test output sheet 9 is an example of an image reading process performed on the test output sheet 9 .
- the test output sheet 9 is an example of an output sheet output from the image forming device 2 .
- the test image g 1 is an example of a target image that is obtained through the image reading process performed on the test output sheet 9 .
- the test image g 1 that is an output image of the image forming device 2
- the sheet noise is a dot-like noise image included in a sheet of the test output sheet 9 itself. If a wrong determination is made with regard to which of the sheet noise and the image defect is the noise point Ps 13 in the test image g 1 , a wrong measure is taken.
- the CPU 80 executes an image defect determination process that is described below (see FIG. 3 to FIG. 5 ). This allows the CPU 80 to determine whether or not the noise point Ps 13 in the test image g 1 is the sheet noise. Furthermore, the CPU 80 determines the state of the image defect based on the test image g 1 from which the sheet noise has been removed.
- images such as the test image g 1 targeted to be processed by the CPU 80 are digital image data.
- the digital image data constitutes, for each of three primary colors, map data that includes a plurality of pixel values corresponding to a two-dimensional coordinate area in a main scanning direction D 1 and a sub scanning direction D 2 crossing the main scanning direction D 1 .
- the three primary colors are, for example, red, green, and blue.
- the sub scanning direction D 2 is perpendicular to the main scanning direction D 1 .
- the main scanning direction D 1 is a horizontal direction in the test image g 1
- the sub scanning direction D 2 is a vertical direction in the test image g 1 .
- the test image g 1 is a mixed-color halftone image that is a combination of a plurality of uniform single-color halftone images that correspond to the plurality of developing colors used in the image forming device 2 .
- the plurality of single-color halftone images are each formed uniformly with a predetermined halftone reference density.
- the original test image g 01 and the test image g 1 are each a mixed-color halftone image that is a combination of a plurality of uniform single-color halftone images that correspond to the plurality of developing colors used in the image forming device 2 .
- the plurality of single-color halftone images are each formed uniformly with a predetermined halftone reference density.
- the original test image g 01 and the test image g 1 are each a mixed-color halftone image that is generated by combining four uniform single-color halftone images that correspond to all developing colors used in the image forming device 2 .
- one test output sheet 9 including one original test image g 01 is output.
- one test image g 1 corresponding to the original test image g 01 is a particular target for identifying the image defect.
- the test image g 1 in the present embodiment is an example of a mixed-color test image.
- the plurality of processing modules of the CPU 80 further include, for the execution of the image defect determination process, a feature image generating portion 8 c , a specific part identifying portion 8 d , a color vector identifying portion 8 e , a periodicity determining portion 8 f , a pattern recognizing portion 8 g , and a noise point determining portion 8 h (see FIG. 2 ).
- S 101 , S 102 , . . . are identification signs representing a plurality of steps of the image defect determination process.
- the main control portion 8 a causes the feature image generating portion 8 c to execute step S 101 of the image defect determination process.
- step S 101 the feature image generating portion 8 c generates the test image g 1 from the read image that was obtained in the image reading process performed on the test output sheet 9 .
- the feature image generating portion 8 c extracts, as the test image g 1 , an original image part from the read image, wherein the original image part is a part of the read image excluding a margin area at the outer edge.
- the feature image generating portion 8 c generates the test image g 1 by performing a compression process to compress the original image part of the read image excluding the margin area at the outer edge and a character part to a predetermined reference resolution. When the resolution of the read image is higher than the reference resolution, the feature image generating portion 8 c compresses the read image. After generating the test image g 1 , the main control portion 8 a moves the process to step S 102 .
- step S 102 the feature image generating portion 8 c starts a specific defect determination process that is described below.
- the specific defect determination process is performed to determine whether or not an image defect such as the vertical stripe Ps 11 , the horizontal stripe Ps 12 , or the noise point Ps 13 is present in the test image g 1 , and determine the cause of the image defect (see FIG. 6 ).
- the main control portion 8 a moves the process to step S 103 .
- step S 103 the main control portion 8 a determines whether or not an image defect has occurred based on the process of step S 102 . Upon determining that an image defect has occurred, the main control portion 8 a moves the process to step S 104 . Otherwise, the main control portion 8 a moves the process to step S 105 .
- step S 104 the main control portion 8 a executes a defect dealing process that had been preliminarily associated with the type and cause of the image defect that was determined to have occurred based on the process of step S 102 .
- the defect dealing process includes either or both of a first dealing process and a second dealing process that are described below.
- the first dealing process is performed to display, on the display portion 802 , a determination result of the cause of the image defect and a message that indicates a measure to be taken in correspondence with the cause of the image defect.
- the first dealing process is an example of a process to notify the determination result of the noise point Ps 13 via a notification device.
- the display portion 802 is an example of the notification device.
- the message that indicates a measure to be taken is, for example, a message that urges replacing a part corresponding to the cause of the image defect.
- the second dealing process is performed to correct an image creation parameter so as to eliminate or alleviate the image defect.
- the image creation parameter is related to the control of the image creating portion 4 x.
- the main control portion 8 a After executing the defect dealing process, the main control portion 8 a ends the image defect determination process.
- step S 105 the main control portion 8 a performs a normality notification to notify that no image defect was identified, and ends the image defect determination process.
- S 201 , S 202 , . . . are identification signs representing a plurality of steps of the specific defect determination process.
- the specific defect determination process starts from step S 201 .
- the feature image generating portion 8 c generates a plurality of feature images g 21 , g 22 , and g 23 by executing a predetermined feature extracting process on the test image g 1 .
- the plurality of feature images g 21 , g 22 , and g 23 are images of specific parts Ps 1 of predetermined particular types extracted from the test image g 1 .
- the plurality of feature images g 21 , g 22 , and g 23 include a first feature image g 21 , a second feature image g 22 , and a third feature image g 23 (see FIG. 6 ).
- the first feature image g 21 is an image of the vertical stripe Ps 11 extracted from the test image g 1 .
- the second feature image g 22 is an image of the horizontal stripe Ps 12 extracted from the test image g 1 .
- the third feature image g 23 is an image of the noise point Ps 13 extracted from the test image g 1 .
- the feature extracting process includes a first pre-process, a second pre-process, and a specific part extracting process.
- each of pixels that are sequentially selected from the test image g 1 is referred to as a focused pixel Px 1 (see FIG. 6 , FIG. 7 ).
- the feature image generating portion 8 c generates a first pre-process image g 11 by executing the first pre-process on the test image g 1 using the main scanning direction D 1 as a processing direction Dx 1 (see FIG. 6 ).
- the feature image generating portion 8 c generates a second pre-process image g 12 by executing the second pre-process on the test image g 1 using the sub scanning direction D 2 as the processing direction Dx 1 (see FIG. 6 ).
- the feature image generating portion 8 c generates the three feature images g 21 , g 22 , and g 23 by executing the specific part extracting process on the first pre-process image g 11 and the second pre-process image g 12 .
- the first pre-process includes a main filter process in which the processing direction Dx 1 is the main scanning direction D 1 .
- the main filter process the pixel value of each of the focused pixels Px 1 sequentially selected from the test image g 1 is converted to a conversion value that is obtained by performing a process to emphasize the difference between a pixel value of a focused area Ax 1 and a pixel value of two adjacent areas Ax 2 that are adjacent to the focused area Ax 1 (see FIG. 6 , FIG. 7 ).
- the focused area Ax 1 includes the focused pixel Px 1 .
- the two adjacent areas Ax 2 are adjacent to the focused area Ax 1 from opposite sides in the processing direction Dx 1 that is preliminarily set for the focused area Ax 1 .
- Each of the focused area Ax 1 and the adjacent areas Ax 2 includes one or more pixels.
- the size of the focused area Ax 1 and the adjacent areas Ax 2 is set based on the width of the vertical stripe Ps 11 or the horizontal stripe Ps 12 to be extracted or the size of the noise point Ps 13 to be extracted.
- Each of the focused area Ax 1 and the adjacent areas Ax 2 occupies the same range in a direction crossing the processing direction Dx 1 .
- the focused area Ax 1 has 21 pixels of three columns and seven rows centered around the focused pixel Px 1 .
- Each of the adjacent areas Ax 2 has 21 pixels of three columns and seven rows, too.
- the number of rows is the number of lines along the processing direction Dx 1
- the number of columns is the number of lines along a direction crossing the processing direction Dx 1 .
- the size of each of the focused area Ax 1 and the adjacent areas Ax 2 is preliminarily set.
- the first correction coefficient K 1 is multiplied with each pixel value of the focused area Ax 1 and is 1 (one) or greater
- the second correction coefficient K 2 is multiplied with each pixel value of the adjacent areas Ax 2 and is less than 0 (zero).
- the first correction coefficient K 1 and the second correction coefficient K 2 are set so that a sum of a value obtained by multiplying the first correction coefficient K 1 by the number of pixels in the focused area Ax 1 and a value obtained by multiplying the second correction coefficient K 2 by the number of pixels in the two adjacent areas Ax 2 becomes zero.
- the feature image generating portion 8 c derives the first correction values respectively corresponding to the pixels of the focused area Ax 1 by multiplying the first correction coefficient K 1 by each pixel value of the focused area Ax 1 , and derives the second correction values respectively corresponding to the pixels of the two adjacent areas Ax 2 by multiplying the second correction coefficient K 2 by each pixel value of the two adjacent areas Ax 2 . Subsequently, the feature image generating portion 8 c derives, as the conversion value for the pixel value of each focused pixel Px 1 , a value by integrating the first correction value and the second correction value.
- the feature image generating portion 8 c derives the conversion value by adding: a total value or an average value of a plurality of first correction values corresponding to a plurality of pixels of the focused area Ax 1 ; and a total value or an average value of a plurality of second correction values corresponding to a plurality of pixels of the two adjacent areas Ax 2 .
- An absolute value of the conversion value is an amplified absolute value of a difference between a pixel value of the focused area Ax 1 and a pixel value of the two adjacent areas Ax 2 .
- the process to derive the conversion value by integrating the first correction value and the second correction value is an example of a process to emphasize the difference between the pixel value of the focused area Ax 1 and the pixel value of two adjacent areas Ax 2 .
- first correction coefficient K 1 may be a negative number
- second correction coefficient K 2 may be a positive number
- the feature image generating portion 8 c may generate, as the first pre-process image g 11 , first main map data that includes a plurality of conversion values that are obtained by performing the main filter process using the main scanning direction D 1 as the processing direction Dx 1 .
- the main filter process in which the processing direction Dx 1 is the main scanning direction D 1 generates the first main map data by extracting either or both of the vertical stripe Ps 11 and the noise point Ps 13 from the test image g 1 .
- the main filter process in which the processing direction Dx 1 is the main scanning direction D 1 generates the first main map data by removing the horizontal stripe Ps 12 from the test image g 1 .
- the vertical stripe Ps 11 corresponds to the first specific part
- the horizontal stripe Ps 12 corresponds to the second specific part
- the noise point Ps 13 corresponds to the third specific part.
- the second pre-process includes the main filter process in which the processing direction Dx 1 is the sub scanning direction D 2 .
- the feature image generating portion 8 c may generate, as the second pre-process image g 12 , second main map data that includes a plurality of conversion values that are obtained by performing the main filter process using the sub scanning direction D 2 as the processing direction Dx 1 .
- the main filter process may derive erroneous conversion values that are reverse in positivity and negativity in edge portions at opposite ends of the specific part Ps 1 in the processing direction Dx 1 with respect to the conversion values indicating the status of the original specific part Ps 1 .
- erroneous conversion values are processed as pixel values indicating the specific part Ps 1 , the determination of the image defect may be adversely affected.
- the second pre-process further includes the edge emphasizing filter process in which the processing direction Dx 1 is the sub scanning direction D 2 , in addition to the main filter process in which the processing direction Dx 1 is the sub scanning direction D 2 .
- an edge emphasizing is performed on the focused area Ax 1 and a predetermined one of the two adjacent areas Ax 2 .
- the pixel value of each of the focused pixels Px 1 sequentially selected from the test image g 1 is converted to an edge intensity that is obtained by integrating a third correction value and a fourth correction value, wherein the third correction value is obtained by correcting the pixel value of each pixel in the focused area Ax 1 by a positive or negative third correction coefficient K 3 , and the fourth correction value is obtained by correcting the pixel value of each pixel in one of the two adjacent areas Ax 2 by a fourth correction coefficient K 4 that is reverse to the third correction coefficient K 3 in positivity and negativity (see FIG. 6 ).
- the third correction coefficient K 3 is a positive coefficient and the fourth correction coefficient K 4 is a negative coefficient.
- the third correction coefficient K 3 and the fourth correction coefficient K 4 are set so that a sum of a value obtained by multiplying the third correction coefficient K 3 by the number of pixels in the focused area Ax 1 and a value obtained by multiplying the fourth correction coefficient K 4 by the number of pixels in the one of the two adjacent areas Ax 2 becomes zero.
- the execution of the edge emphasizing filter process by using the main scanning direction D 1 as the processing direction Dx 1 generates horizontal edge strength map data in which the pixel value of each pixel in the test image g 1 has been converted to the edge strength.
- the execution of the edge emphasizing filter process by using the sub scanning direction D 2 as the processing direction Dx 1 generates vertical edge strength map data in which the pixel value of each pixel in the test image g 1 has been converted to the edge strength.
- the feature image generating portion 8 c generates the first main map data by executing the main filter process using the main scanning direction D 1 as the processing direction Dx 1 .
- the feature image generating portion 8 c generates the horizontal edge strength map data by executing the edge emphasizing filter process using the main scanning direction D 1 as the processing direction Dx 1 .
- the feature image generating portion 8 c generates the first pre-process image g 11 by correcting each pixel value of the first main map data by each corresponding pixel value of the horizontal edge strength map data. For example, the feature image generating portion 8 c generates the first pre-process image g 11 by adding an absolute value of each pixel value of the horizontal edge strength map data to each pixel value of the first main map data.
- the feature image generating portion 8 c generates the second main map data by executing the main filter process using the sub scanning direction D 2 as the processing direction Dx 1 .
- the feature image generating portion 8 c generates the vertical edge strength map data by executing the edge emphasizing filter process using the sub scanning direction D 2 as the processing direction Dx 1 .
- the feature image generating portion 8 c generates the second pre-process image g 12 by correcting each pixel value of the second main map data by each corresponding pixel value of the vertical edge strength map data. For example, the feature image generating portion 8 c generates the second pre-process image g 12 by adding an absolute value of each pixel value of the vertical edge strength map data to each pixel value of the second main map data.
- the three feature images g 21 , g 22 , and g 23 are generated by extracting the vertical stripe Ps 11 , the horizontal stripe Ps 12 , and the noise point Ps 13 individually from the first pre-process image g 11 or the second pre-process image g 12 .
- the three feature images g 21 , g 22 , and g 23 are the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 .
- the first feature image g 21 includes, among specific parts Ps 1 which are each composed of one or more significant pixels and are present in the first pre-process image g 11 and the second pre-process image g 12 , a specific part Ps 1 that is present in the first pre-process image g 11 and is not common to the first pre-process image g 11 and the second pre-process image g 12 .
- the first feature image g 21 does not include the horizontal stripe Ps 12 and the noise point Ps 13 .
- the first pre-process image g 11 includes the vertical stripe Ps 11
- the first feature image g 21 includes the vertical stripe Ps 11 .
- the second feature image g 22 is formed by extracting, among the specific parts Ps 1 that are present in the first pre-process image g 11 and the second pre-process image g 12 , a specific part Ps 1 that is present in the second pre-process image g 12 and is not common to the first pre-process image g 11 and the second pre-process image g 12 .
- the second feature image g 22 does not include the vertical stripe Ps 11 and the noise point Ps 13 .
- the second pre-process image g 12 includes the horizontal stripe Ps 12
- the second feature image g 22 includes the horizontal stripe Ps 12 .
- the third feature image g 23 is formed by extracting a specific part Ps 1 that is common to the first pre-process image g 11 and the second pre-process image g 12 .
- the third feature image g 23 does not include the vertical stripe Ps 11 and the horizontal stripe Ps 12 .
- the third feature image g 23 includes the noise point Ps 13 .
- the feature image generating portion 8 c derives an index value Zi by applying a first pixel value Xi and a second pixel value Yi to the following formula (1), wherein the first pixel value Xi represents each pixel value that exceeds a predetermined reference value in the first pre-process image g 11 , and the second pixel value Yi represents each pixel value that exceeds the reference value in the second pre-process image g 12 .
- the subscription “i” denotes the position identification number of each pixel.
- the index value Zi of each pixel constituting the vertical stripe Ps 11 is a relatively large positive number.
- the index value Zi of each pixel constituting the horizontal stripe Ps 12 is a relatively small negative number.
- the index value Zi of each pixel constituting the noise point Ps 13 is 0 (zero) or a value close to 0 (zero).
- the index value Zi is an example of an index value of a difference between each pixel value of the first pre-process image g 11 and each corresponding pixel value of the second pre-process image g 12 .
- index value Zi can be used to simplify the process of extracting the vertical stripe Ps 11 from the first pre-process image g 11 , extracting the horizontal stripe Ps 12 from the second pre-process image g 12 , and extracting the noise point Ps 13 from the first pre-process image g 11 or the second pre-process image g 12 .
- the feature image generating portion 8 c generates the first feature image g 21 by converting the first pixel value Xi in the first pre-process image g 11 to a first specificity Pi that is derived by the following formula (2). This generates the first feature image g 21 that includes the vertical stripe Ps 11 extracted from the first pre-process image g 11 .
- [Math 2] Pi XiZi (2)
- the feature image generating portion 8 c generates the second feature image g 22 by converting the second pixel value Yi in the second pre-process image g 12 to a second specificity Qi that is derived by the following formula (3). This generates the second feature image g 22 that includes the horizontal stripe Ps 12 extracted from the second pre-process image g 12 .
- Qi Yi ( ⁇ Zi ) (3)
- the feature image generating portion 8 c may generate the third feature image g 23 by converting the second pixel value Yi in the second pre-process image g 12 to the third specificity Ri that is derived by the following formula (5). This generates the third feature image g 23 that includes the noise point Ps 13 extracted from the second pre-process image g 12 .
- Ri Yi ( Zi ⁇ 1) (5)
- the feature image generating portion 8 c generates the first feature image g 21 by converting each pixel value in the first pre-process image g 11 by a predetermined formula (2) that is based on the index value Zi.
- the formula (2) is an example of a first conversion formula.
- the feature image generating portion 8 c generates the second feature image g 22 by converting each pixel value in the second pre-process image g 12 by a predetermined formula (3) that is based on the index value Zi.
- the formula (3) is an example of a second conversion formula.
- the feature image generating portion 8 c generates the third feature image g 23 by converting each pixel value in the first pre-process image g 11 or the second pre-process image g 12 by a predetermined formula (4) or formula (5) that is based on the index value Zi.
- the formula (4) and the formula (5) are each an example of a third conversion formula.
- step S 201 in which the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 are generated is an example of a process in which the vertical stripe Ps 11 , the horizontal stripe Ps 12 , and the noise point Ps 13 of the one or more specific parts Ps 1 are extracted as the image defects from the first pre-process image g 11 and the second pre-process image g 12 .
- step S 201 in which the third feature image g 23 is generated is an example of a process to generate an extraction image by extracting, as the noise point Ps 13 , a specific part Ps 1 among the one or more specific parts Ps 1 that is common to the first pre-process image g 11 and the second pre-process image g 12 from the first pre-process image g 11 and the second pre-process image g 12 .
- the third feature image g 23 is an example of the extraction image.
- the feature image generating portion 8 c moves the process to step S 202 .
- step S 202 the specific part identifying portion 8 d identifies the positions of the specific parts Ps 1 in the feature images g 21 , g 22 , and g 23 .
- the processes of steps S 201 and S 202 are an example of a process to identify the specific part Ps 1 that is composed of a plurality of significant pixels in the test image g 1 .
- the specific part identifying portion 8 d determines, as the specific part Ps 1 , a part that includes a pixel value that is out of a predetermined reference range in the feature images g 21 , g 22 , and g 23 .
- the vertical stripe Ps 11 is identified from the first feature image g 21
- the horizontal stripe Ps 12 is identified from the second feature image g 22
- the noise point Ps 13 is identified from the third feature image g 23 .
- the specific part identifying portion 8 d executes a coupling process on each of the feature images g 21 , g 22 , and g 23 .
- the coupling process when a plurality of specific parts Ps 1 are present in a predetermined proximity range in the main scanning direction D 1 or the sub scanning direction D 2 , the specific parts Ps 1 are coupled into one series of specific parts Ps 1 .
- the specific part identifying portion 8 d executes the coupling process to couple the two vertical stripes Ps 11 into one vertical stripe Ps 11 .
- the specific part identifying portion 8 d executes the coupling process to couple the two horizontal stripes Ps 12 into one horizontal stripe Ps 12 .
- the specific part identifying portion 8 d executes the coupling process to couple the plurality of noise points Ps 13 into one noise point Ps 13 .
- the specific part identifying portion 8 d ends the specific defect determination process when, in the process of step S 202 , it has failed to identify a position of the specific part Ps 1 in any of the three feature images g 21 , g 22 , and g 23 .
- the specific part identifying portion 8 d moves the process to step S 203 when it has identified a position of the specific part Ps 1 in one or more of the three feature images g 21 , g 22 , and g 23 .
- the color vector identifying portion 8 e identifies a detection color vector that represents a vector in a color space from one of a color of the specific part Ps 1 in the test image g 1 and a color of a reference area including the periphery of the specific part Ps 1 to the other.
- the reference area is an area of a predetermined range decided on the basis of the specific part Ps 1 .
- the reference area includes a peripheral area adjacent to the specific part Ps 1 and does not include the specific part Ps 1 .
- the reference area may include the specific part Ps 1 and a peripheral area adjacent to the specific part Ps 1 .
- the test image g 1 is originally a uniform halftone image. As a result, when an excellent test image g 1 is formed on the test output sheet 9 , the specific part Ps 1 is not identified, and the color vector at any position in the test image g 1 is approximately zero vector.
- the direction of the detection color vector between the specific part Ps 1 and the reference area corresponding to the specific part Ps 1 indicates an excess or a shortage of the toner density in any of the four developing colors in the image forming device 2 .
- the direction of the detection color vector indicates, as the cause of the specific part Ps 1 , any of the four image creating portions 4 x in the image forming device 2 .
- the color vector identifying portion 8 e may identify, as the detection color vector, a vector in a color space from one of a color of the specific part Ps 1 in the test image g 1 and a predetermined reference color to the other.
- the reference color is the original color of the test image g 1 .
- step S 203 the color vector identifying portion 8 e , based on the detection color vector, further determines a developing color that is the cause of the specific part Ps 1 , and the excess/shortage state of the density of the developing color.
- the secondary storage device 82 preliminarily stores information of a plurality of unit vectors that indicate, for each of cyan, magenta, yellow, and black, the directions in which the density increases and decreases with respect to the reference color of the test image g 1 .
- the color vector identifying portion 8 e normalizes the detection color vector to a predetermined unit length. Furthermore, the color vector identifying portion 8 e determines which of the plurality of unit vectors approximates most closely to the detection color vector after the normalization. This allows the color vector identifying portion 8 e to determine a developing color that is the cause of the specific part Ps 1 and the excess/shortage state of the density of the developing color.
- step S 203 After executing the process of step S 203 , the color vector identifying portion 8 e moves the process to step S 204 .
- step S 204 the periodicity determining portion 8 f moves the process to step S 205 when the specific part Ps 1 has been identified in either or both of the second feature image g 22 and the third feature image g 23 . Otherwise, the periodicity determining portion 8 f moves the process to step S 207 .
- either or both of the second feature image g 22 and the third feature image g 23 in which the specific part Ps 1 has been identified is referred to as a periodicity determination image.
- the specific part Ps 1 in the periodicity determination image is the horizontal stripe Ps 12 or the noise point Ps 13 (see FIG. 6 ).
- step S 205 the noise point determining portion 8 h executes a sheet noise removal process on the third feature image g 23 .
- the sheet may have a dot-like sheet noise that is formed in the manufacturing stage. Since the sheet noise is not an image defect, the sheet noise needs to be removed from a target of determination of the cause of the image defect.
- the sheet noise is detected from the noise points Ps 13 in the third feature image g 23 , and the sheet noise is removed from the target of determination of the cause of the image defect.
- the noise point determining portion 8 h moves the process to step S 206 .
- the noise point Ps 13 that is the target of determination of the cause of the image defect in the following steps S 206 to step S 208 is the noise point Ps 13 that has not been determined as a sheet noise.
- the periodicity determining portion 8 f executes a periodic specific part determination process on the periodicity determination image.
- the periodic specific part determination process includes a number determination process, a specific part periodicity determination process, and a specific part periodicity cause determination process.
- the number determination process is a process to determine the number of specific parts Ps 1 that are lined up in the sub scanning direction D 2 in the periodicity determination image.
- the periodicity determining portion 8 f determines the number of horizontal stripes Ps 12 that are lined up in the sub scanning direction D 2 , by counting, in the second feature image g 22 , the number of horizontal stripes Ps 12 lined up in the sub scanning direction D 2 in which parts occupying the same range in the main scanning direction D 1 exceed a predetermined ratio.
- the periodicity determining portion 8 f determines the number of noise points Ps 13 lined up in the sub scanning direction D 2 in the third feature image g 23 , by counting the number of noise points Ps 13 of which positional shift in the main scanning direction D 1 is within a predetermined range, among the noise points Ps 13 that are lined up in the sub scanning direction D 2 .
- the periodicity determining portion 8 f executes the specific part periodicity determination process only on two or more specific parts Ps 1 that are lined up in the sub scanning direction D 2 .
- the periodicity determining portion 8 f determines that one specific part Ps 1 lined up in the sub scanning direction D 2 does not have periodicity, and skips the specific part periodicity determination process and the specific part periodicity cause determination process for such specific part Ps 1 .
- the periodicity corresponds to the outer peripheral length of the rotating body related to the image creation, such as the photoconductor 41 , the charging roller 42 a , the developing roller 43 a , or the primary transfer rollers 441 that are provided in each of the image creating portions 4 x or the transfer device 44 .
- the state of the rotating bodies related to the image creation influences the quality of the image formed on the sheet.
- the rotating bodies related to the image creation are referred to as image creation rotating bodies.
- the periodicity corresponding to the outer peripheral length of the image creation rotating body may appear as an interval in the sub scanning direction D 2 between a plurality of horizontal stripes Ps 12 or a plurality of noise points Ps 13 .
- the periodicity determination image has the periodicity corresponding to the outer peripheral length of an image creation rotating body, it can be said that the image creation rotating body corresponding to the periodicity is the cause of the horizontal stripes Ps 12 or the noise points Ps 13 in the periodicity determination image.
- the periodicity determining portion 8 f executes an interval deriving process as the specific part periodicity determination process.
- the periodicity determining portion 8 f executes a frequency analyzing process as the specific part periodicity determination process.
- the periodicity determining portion 8 f identifies a specific part frequency with respect to the periodicity determination image that includes three or more specific parts Ps 1 lined up in the sub scanning direction D 2 .
- the specific part frequency is a dominant frequency in a frequency distribution of a data sequence of the specific part Ps 1 in the periodicity determination image.
- the periodicity determining portion 8 f identifies the specific part frequency by performing a frequency analysis such as the Fourier transformation.
- the periodicity determining portion 8 f derives, as the period of the three or more specific parts Ps 1 , a period corresponding to the specific part frequency.
- the periodicity determining portion 8 f determines, for each of a plurality of predetermined candidates for image creation rotating body, whether or not the outer peripheral length of each candidate satisfies a predetermined period approximate condition with respect to the period of the specific part Ps 1 .
- the plurality of candidates for image creation rotating body in step S 206 is an example of a plurality of predetermined cause candidates corresponding to the horizontal stripe Ps 12 or the noise point Ps 13 .
- a specific part Ps 1 corresponding to any one of the candidates for image creation rotating body that was determined to satisfy the period approximate condition is referred to as a periodic specific part, and the other specific parts Ps 1 are referred to as non-periodic specific parts.
- the periodic specific part and the non-periodic specific parts are specific parts Ps 1 included in the second feature image g 22 or the third feature image g 23 .
- the periodicity determining portion 8 f determines that one of the candidates for image creation rotating body that was determined to satisfy the period approximate condition is the cause of the periodic specific part. This determines the cause of the horizontal stripe Ps 12 or the noise point Ps 13 .
- step S 206 the periodicity determining portion 8 f determines, based on the detection color vector determined in step S 204 , which of the image creation rotating bodies of the four image creating portions 4 x of different developing colors is the cause of the horizontal stripe Ps 12 or the noise point Ps 13 .
- the periodicity determining portion 8 f selects the non-periodic specific part as a target of a feature pattern recognition process that is described below.
- the periodicity determining portion 8 f generates inverse Fourier transformation data by applying an inverse Fourier transformation to the frequency distribution obtained by the Fourier transformation from which frequency components other than the specific part frequency have been removed.
- the periodicity determining portion 8 f identifies, as a non-periodic specific part, a specific part Ps 1 that is, among three or more specific parts Ps 1 lined up in the sub scanning direction D 2 , located at a position out of a peak position in the waveform in the sub scanning direction D 2 indicated by the inverse Fourier transformation data.
- the periodicity determining portion 8 f ends the specific defect determination process.
- the periodicity determining portion 8 f moves the process to step S 207 .
- step S 207 the pattern recognizing portion 8 g executes the feature pattern recognition process on the first feature image g 21 and each of the second feature image g 22 and the third feature image g 23 that each include a non-periodic specific part.
- the first feature image g 21 and the second feature image g 22 and the third feature image g 23 that each include a non-periodic specific part that has not been subjected to the determination are treated as input images.
- the pattern recognizing portion 8 g performs a pattern recognition on each input image to determine which of a plurality of predetermined cause candidates corresponding to the image defects corresponds to the input image.
- the input image of the feature pattern recognition process may include the horizontal edge strength map data or the vertical edge strength map data obtained in the edge emphasizing filter process.
- the first feature image g 21 and the horizontal edge strength map data may be used as the input image in the feature pattern recognition process to determine the vertical stripe Ps 11 .
- the second feature image g 22 and the vertical edge strength map data may be used as the input image in the feature pattern recognition process to determine the horizontal stripe Ps 12 .
- the third feature image g 23 and either or both of the horizontal edge strength map data and the vertical edge strength map data may be used as the input image in the feature pattern recognition process to determine the noise point Ps 13 .
- a classification-type machine learning algorithm called random forests
- a machine learning algorithm called SVM Small Vector Machine
- a CNN Convolutional Neural Network
- the learning model is prepared individually for each of the first feature image g 21 and each of the second feature image g 22 and the third feature image g 23 that each include the non-periodic specific part.
- the plurality of sample images are used as the teacher data for each of the cause candidates.
- step S 207 the pattern recognizing portion 8 g determines, based on the detection color vector identified in step S 204 , which of the four image creating portions 4 x having different developing colors has a part that is the cause of the vertical stripe Ps 11 , the horizontal stripe Ps 12 , or the noise point Ps 13 .
- step S 207 the cause of the vertical stripe Ps 11 and the causes of the horizontal stripe Ps 12 and the noise point Ps 13 that were identified as the non-periodic specific parts are determined. After executing the process of step S 207 , the pattern recognizing portion 8 g ends the specific defect determination process.
- step S 207 is an example of a process to determine the cause of the vertical stripe Ps 11 , the horizontal stripe Ps 12 , or the noise point Ps 13 .
- S 301 , S 302 , . . . are identification signs representing a plurality of steps of the sheet noise removal process.
- the sheet noise removal process starts from step S 301 .
- noise point Ps 13 that is processed in the sheet noise removal process is the noise point Ps 13 that has been identified as the non-periodic specific part.
- step S 301 the noise point determining portion 8 h selects a target sheet from a plurality of predetermined sheet candidates in accordance with selection information that is input via the operation portion 801 .
- the noise point determining portion 8 h displays, on the display portion 802 , a sheet selection screen that includes, as a selection menu, information of the plurality of sheet candidates, and executes a process to input the selection information.
- the plurality of sheet candidates are candidates for a sheet type of the test output sheet 9 .
- the sheet type is determined in accordance with one or more of sheet material, product name or model, and manufacturing lot.
- step S 301 the noise point determining portion 8 h selects the target sheet corresponding to the test output sheet 9 from the plurality of sheet candidates.
- the image forming device 2 may include a plurality of sheet storage portions 21 , and the secondary storage device 82 may preliminarily store storage sheet data that indicates types of the sheets stored in the plurality of sheet storage portions 21 .
- the storage sheet data may include data of the plurality of sheet candidates that correspond to the sheets stored in the plurality of sheet storage portions 21 .
- the noise point determining portion 8 h may select, as the target sheet, one of the plurality of sheet candidates in the storage sheet data that corresponds to one of the plurality of sheet storage portions 21 from which the sheet is supplied in the test print process.
- the RAM 81 is an example of an input device that inputs information for selecting the target sheet.
- step S 301 After executing the process of step S 301 , the noise point determining portion 8 h moves the process to step S 302 .
- step S 302 the noise point determining portion 8 h derives feature information regarding the noise points Ps 13 included in the third feature image g 23 .
- the feature information is an example of information regarding the noise point Ps 13 included in the test image g 1 .
- generation of the third feature image g 23 by the specific part identifying portion 8 d in step S 201 is a part of a process to derive the feature information.
- the noise point determining portion 8 h derives, as the feature information, at least one of the degree of flatness of the noise point Ps 13 and a color vector difference of the noise point Ps 13 .
- the noise point determining portion 8 h derives, as the degree of flatness, a ratio of the length of the noise point Ps 13 in the main scanning direction D 1 to the length of the noise point Ps 13 in the sub scanning direction D 2 , the roundness, or the flatness.
- the noise point determining portion 8 h may identify a longitudinal direction DL 1 of the noise point Ps 13 and derive, as the degree of flatness, a ratio of the length of the noise point Ps 13 in the longitudinal direction DL 1 to the length of the noise point Ps 13 in a short direction that is perpendicular to the longitudinal direction DL 1 (see FIG. 8 A to FIG. 8 C ).
- the color vector difference is an index value that indicates the degree of coincidence or difference of the detection color vector of the noise point Ps 13 with respect to each of four predetermined reference color vectors.
- the four reference color vectors are predetermined vectors in the color space corresponding to the four developing colors used in the image forming device 2 .
- the four reference color vectors are unit vectors that respectively represent four colors of cyan, magenta, yellow, and black in the color space.
- the four reference color vectors are an example of one or more pieces of predetermined reference color information corresponding to one or more developing colors used in the image forming device 2 .
- the detection color vector of the noise point Ps 13 is a vector in a color space from one of a color of the specific part Ps 1 in the third feature image g 23 and a color of the reference area to the other.
- the detection color vector is normalized to a predetermined unit length.
- the color vector difference may be an angle formed by the detection color vector and each of the reference color vectors. In this case, the closer to 0 degrees the color vector difference is, the mores to the developing color the color of the noise point Ps 13 approximates. In addition, the closer to 180 degrees the color vector difference is, the more different from the developing color the color of the noise point Ps 13 is.
- the color vector difference is an example of information indicating the degree of coincidence or difference of the detection color vector with respect to each of the four reference color vectors.
- the detection color vector is an example of detection color information corresponding to the color of the noise point Ps 13 in the third feature image g 23 .
- the color of the sheet noise may be different from any of the developing colors of the image forming device 2 .
- the sheet noise is often distinguishable from the noise point Ps 13 that is generated due to the image defect.
- the sheet noise is often distinguishable from the noise point Ps 13 that is generated due to the image defect.
- the noise point determining portion 8 h derives, as the feature information, the edge strength and the number of edges of the noise point Ps 13 , in addition to the color vector difference and the degree of flatness.
- a pixel sequence that traverses the noise point Ps 13 in the third feature image g 23 is referred to as a transverse pixel value sequence AR 1 (see FIG. 8 A to FIG. 8 C ).
- the edge strength represents the degree of difference between the pixel value V 1 of the noise point Ps 13 and the pixel value V 1 of an adjacent area AN 1 adjacent to the noise point Ps 13 in the third feature image g 23 (see FIG. 8 A to FIG. 8 C ).
- the noise point determining portion 8 h derives, as the edge strength, a difference between the representative value of the pixel values V 1 of the noise point Ps 13 and the representative value of the pixel values V 1 of the adjacent area AN 1 .
- the noise point determining portion 8 h may derive, as the edge strength, the pixel value V 1 of an outer edge part of the noise point Ps 13 in an image that is obtained by executing a well-known edge emphasizing process on the third feature image g 23 .
- the edge strength of the noise point Ps 13 that is generated due to the image defect indicates a steeper edge than the edge strength of the sheet noise.
- the number of edges is the number of positions in the transverse pixel sequence AR 1 where the variation of the pixel value V 1 exceeds an acceptable range, the transverse pixel sequence AR 1 being a pixel sequence that traverses the noise point Ps 13 in the third feature image g 23 (see FIG. 8 A to FIG. 8 C ).
- the noise point determining portion 8 h sets a transverse line L 1 that traverses the noise point Ps 13 and identifies, as the transverse pixel sequence AR 1 , a pixel sequence along the transverse line L 1 in the noise point Ps 13 and two adjacent areas AN 1 located at opposite sides of the noise point Ps 13 .
- the noise point determining portion 8 h sets, as the transverse line L 1 , a line that passes through the center of the noise point Ps 13 in the short direction and extends along the longitudinal direction DL 1 .
- the noise point determining portion 8 h derives, as the number of edges, the number of positions in the transverse pixel sequence AR 1 where the variation of the pixel value V 1 exceeds an acceptable range.
- the noise point determining portion 8 h derives, as the number of edges, the number of positions in the transverse pixel sequence AR 1 where a differential value dV 1 of the pixel value V 1 is out of a predetermined first reference range RA 1 (see FIG. 8 A to FIG. 8 C ).
- the noise point determining portion 8 h may derive, as the number of edges, the number of positions in the transverse pixel sequence AR 1 where the pixel value V 1 crosses a reference value VA 1 or a second reference range RA 2 (see FIG. 8 A to FIG. 8 C ).
- the noise point determining portion 8 h sets the reference value VA 1 or the second reference range RA 2 based on the pixel value V 1 of the adjacent area AN 1 . It is noted that the pixel value V 1 of the adjacent area AN 1 is an example of the pixel value V 1 of the transverse pixel sequence AR 1 .
- FIG. 8 A to FIG. 8 C show examples of the noise point Ps 13 generated due to the abnormal developing.
- the abnormal developing is a phenomenon in which dot-like defective toner images are developed on the surface of the photoconductor 41 by the developing device 43 of the image forming device 2 and are further transferred therefrom to the sheet. That is, the noise point Ps 13 generated due to the abnormal developing is a type of image defect.
- FIG. 8 A shows a case where the symptom of the noise point Ps 13 is mild.
- FIG. 8 B shows a case where the symptom of the noise point Ps 13 is middle.
- FIG. 8 C shows a case where the symptom of the noise point Ps 13 is serious.
- step S 302 After executing the process of step S 302 , the noise point determining portion 8 h moves the process to step S 303 .
- step S 303 the noise point determining portion 8 h selects a target algorithm corresponding to the target sheet, from a plurality of determination algorithms that respectively correspond to the plurality of sheet candidates.
- the plurality of determination algorithms respectively corresponding to the plurality of sheet candidates are used to determine whether or not the noise point Ps 13 is the sheet noise.
- the plurality of determination algorithms are used to determine whether or not the feature information of the noise point Ps 13 derived in step S 302 is information representing the sheet noise.
- the determination algorithms are used to determine whether or not the noise point Ps 13 is the sheet noise by comparing a value of the feature information or an evaluation value of the feature information with a predetermined threshold.
- the determination algorithms may be a learning model whose input parameter is the value of the feature information or the evaluation value of the feature information.
- the learning model has been preliminarily learned using, as teacher data, a plurality of pieces of sample data corresponding to sheet noises.
- the sample data corresponds to the feature information.
- the learning model is prepared individually for each of the plurality of sheet candidates.
- the random forests, the SVM, or the CNN algorithm may be adopted in the learning model.
- the secondary storage device 82 preliminarily stores algorithm data DT 1 that indicates the plurality of sheet candidates and the plurality of determination algorithms respectively corresponding thereto.
- step S 301 the noise point determining portion 8 h generates the selection menu of the sheet selection screen based on the algorithm data DT 1 . Furthermore, in step S 303 , the noise point determining portion 8 h selects, as the target algorithm, one of the plurality of determination algorithms that corresponds to the target sheet.
- step S 303 After executing the process of step S 303 , the noise point determining portion 8 h moves the process to step S 304 .
- step S 304 the noise point determining portion 8 h executes a sheet noise determination process.
- the sheet noise determination process it is determined whether or not the noise point Ps 13 is the sheet noise by applying the feature information derived in step S 302 to the target algorithm.
- step S 304 After executing the process of step S 304 , the noise point determining portion 8 h moves the process to step S 305 .
- step S 305 the noise point determining portion 8 h removes the noise point Ps 13 that was determined as the sheet noise, from the target of determination of the cause of the image defect performed in step S 207 .
- step S 207 of determining the cause of the noise point Ps 13 is executed on the noise point Ps 13 that was not determined as the sheet noise in the third feature image g 23 .
- the noise point determining portion 8 h ends the sheet noise removal process.
- the noise point determining portion 8 h executes the sheet noise determination process of step S 304 by applying the feature information to the target algorithm that corresponds to the type of the sheet of the test output sheet 9 itself. This makes it possible to determine with high accuracy whether or not the noise point Ps 13 in the third feature image g 23 is the sheet noise.
- step S 302 the noise point determining portion 8 h derives, as the feature information, at least one of the degree of flatness of the noise point Ps 13 and the edge strength of the noise point Ps 13 in the third feature image g 23 .
- the noise point determining portion 8 h identifies, as the feature information, the number of edges and the color vector difference, too.
- the degree of flatness, the edge strength, the number of edges, and the color vector difference are effective information for distinguishing the sheet noise from the image defect.
- the following describes a first application example of the image processing apparatus 10 .
- the noise point determining portion 8 h derives, as the feature information, an image of a target area including the noise point Ps 13 in the third feature image g 23 .
- the target area is of predetermined shape and size centered around the noise point Ps 13 .
- the determination algorithms of the present application example represent a sheet noise pattern recognition process in which the image of the target area is treated as an input image, and it is determined whether or not the input image is an image corresponding to the sheet noise by performing a pattern recognition on the input image.
- the noise point determining portion 8 h determines whether or not the noise point Ps 13 is the sheet noise by executing the sheet noise pattern recognition process using the image of the target area as the input image.
- the sheet noise pattern recognition process of the present application example it is determined whether or not an input image corresponds to the sheet noise based on a learning model that has been preliminarily learned using, as teacher data, a plurality of sample images corresponding to sheet noises.
- the random forests, the SVM, or the CNN algorithm may be adopted in the learning model.
- S 401 , S 402 , . . . are identification signs representing a plurality of steps of the feature image generating process of the present application example.
- the feature image generating process of the present application example starts from step S 401 .
- step S 401 the feature image generating portion 8 c selects, from a plurality of predetermined compression ratio candidates, a compression ratio to be adopted and moves the process to step S 402 .
- step S 402 the feature image generating portion 8 c generates the test image g 1 by compressing the read image with the selected compression ratio.
- the processes of steps S 401 and S 402 are an example of a compression process. Thereafter, the feature image generating portion 8 c moves the process to step S 403 .
- step S 403 the feature image generating portion 8 c generates the first pre-process image g 11 by executing the first pre-process on the compressed test image g 1 obtained in step S 402 . Thereafter, the feature image generating portion 8 c moves the process to step S 404 .
- step S 404 the feature image generating portion 8 c generates the second pre-process image g 12 by executing the second pre-process on the compressed test image g 1 obtained in step S 402 . Thereafter, the feature image generating portion 8 c moves the process to step S 405 .
- step S 405 upon determining that the processes of steps S 401 to S 404 have been executed with all of the plurality of compression ratio candidates, the feature image generating portion 8 c moves the process to step S 406 . Otherwise, the feature image generating portion 8 c executes the processes of steps S 401 to S 404 with a different compression ratio.
- the feature image generating portion 8 c In the compression process of steps S 401 and S 402 , the feature image generating portion 8 c generates a plurality of test images g 1 of different sizes by compressing the read image with a plurality of different compression ratios.
- the feature image generating portion 8 c generates a plurality of first pre-process images g 11 and a plurality of second pre-process images g 12 that respectively correspond to the plurality of test images g 1 by executing the first pre-process and the second pre-process on the plurality of test images g 1 .
- step S 406 the feature image generating portion 8 c executes the specific part extracting process on each of the plurality of first pre-process images g 11 and the plurality of second pre-process images g 12 . This allows the feature image generating portion 8 c to generate a plurality of candidates for each of the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 corresponding to the plurality of test images g 1 . Thereafter, the feature image generating portion 8 c moves the process to step S 407 .
- step S 407 the feature image generating portion 8 c generates the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 by integrating the plurality of candidates obtained in step S 406 . Thereafter, the feature image generating portion 8 c ends the feature image generating process.
- the feature image generating portion 8 c sets, as each pixel value of the first feature image g 21 , a representative value such as the maximum value or the average value of the pixel values of the plurality of candidates for the first feature image g 21 . This also applies to the second feature image g 22 and the third feature image g 23 .
- steps S 401 to S 404 are an example of a process to generate a plurality of first pre-process images g 11 and a plurality of second pre-process images g 12 by executing the first pre-process and the second pre-process multiple times with different size ratios of the size of the test image g 1 to that of the focused area Ax 1 and the adjacent areas Ax 2 .
- Changing the compression ratio is an example of changing the size ratio between the size of the test image g 1 and the size of the focused area Ax 1 and the adjacent areas Ax 2 .
- steps S 406 to S 407 are an example of a process to generate the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 by performing the specific part extracting process based on the plurality of first pre-process images g 11 and the plurality of second pre-process images g 12 .
- the first pre-process and the second pre-process of the present application example are an example of a process to generate a plurality of first pre-process images g 11 and a plurality of second pre-process images g 12 by executing the first pre-process and the second pre-process multiple times with different size ratios of the size of the test image g 1 to that of the focused area Ax 1 and the adjacent areas Ax 2 .
- the feature image generating portion 8 c may generate a plurality of first pre-process images g 11 and a plurality of second pre-process images g 12 by executing the first pre-process and the second pre-process multiple times by changing the filter size.
- the feature image generating portion 8 c sets, as each pixel value of the integrated first feature image g 21 , a representative value such as the maximum value or the average value of the pixel values of the plurality of first pre-process images g 11 . This also applies to the plurality of second pre-process images g 12 .
- the feature image generating portion 8 c may execute the specific part extracting process on the integrated first pre-process image g 11 and the integrated second pre-process image g 12 . This generates the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 .
- the feature image generating portion 8 c identifies, as the specific part Ps 1 , a part in which the pixel values of the first pre-process image g 11 and the second pre-process image g 12 are out of a predetermined reference range.
- the feature image generating portion 8 c performs the specific part extracting process to identify the specific part Ps 1 based on the size of each pixel value of the first pre-process image g 11 and the second pre-process image g 12 .
- the process of the present application example performed by the feature image generating portion 8 c is an example of a process to identify the specific part Ps 1 composed of one or more significant pixels in the test image g 1 .
- the feature image generating portion 8 c extracts the vertical stripe Ps 11 by removing, from the specific part Ps 1 of the first pre-process image g 11 , the specific part Ps 1 that is common to the first pre-process image g 11 and the second pre-process image g 12 .
- the feature image generating portion 8 c extracts the horizontal stripe Ps 12 by removing, from the specific part Ps 1 of the second pre-process image g 12 , the specific part Ps 1 that is common to the first pre-process image g 11 and the second pre-process image g 12 .
- the feature image generating portion 8 c extracts, as the noise point Ps 13 , the specific part Ps 1 that is common to the first pre-process image g 11 and the second pre-process image g 12 .
- the feature image generating portion 8 c generates the first feature image g 21 by converting the first pixel value Xi that was not determined as the vertical stripe Ps 11 in the first pre-process image g 11 , into an interpolation value based on the surrounding pixel values.
- the feature image generating portion 8 c generates the second feature image g 22 by converting the second pixel value Yi that was not determined as the horizontal stripe Ps 12 in the second pre-process image g 12 , into an interpolation value based on the surrounding pixel values.
- the feature image generating portion 8 c generates the third feature image g 23 by converting the first pixel value Xi that was not determined as the noise point Ps 13 in the first pre-process image g 11 , into an interpolation value based on the surrounding pixel values.
- the feature image generating portion 8 c may generate the third feature image g 23 by converting the second pixel value Yi that was not determined as the noise point Ps 13 in the second pre-process image g 12 , into an interpolation value based on the surrounding pixel values.
- the image sensor 1 a may be difficult, depending on the density state of each color, for the image sensor 1 a to correctly detect a gradation level of a yellow part in an image that is a mixture of yellow and other colors. Similarly, it may be difficult, depending on the density state of each color, for the image sensor 1 a to correctly detect a gradation level of a chromatic color part in an image that is a mixture of black and chromatic colors.
- the job control portion 8 b performs the test print process to output two or three test output sheets 9 with different types of original test images g 01 formed thereon.
- a sheet on which a mixed-color test image has been formed and a sheet on which the original gray test image has been formed are output, wherein the mixed-color test image is a combination of a uniform cyan single-color halftone image, a uniform magenta single-color halftone image, and a uniform yellow single-color halftone image.
- the test image g 1 of the present application example includes the mixed-color test image, the yellow test image, and the gray test image that respectively correspond to the original mixed-color test image, the original yellow test image, and the original gray test image.
- the feature image generating portion 8 c generates the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 for each of the mixed-color test images and the single-color test images read from a plurality of test output sheets 9 .
- the color vector identifying portion 8 e , the periodicity determining portion 8 f , and the pattern recognizing portion 8 g execute a process to determine the causes of the image defects, by using the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 corresponding to the mixed-color test images.
- the periodicity determining portion 8 f and the pattern recognizing portion 8 g execute a process to determine the causes of the image defects, by using the first feature image g 21 , the second feature image g 22 , and the third feature image g 23 corresponding to the single-color test images.
- the periodicity determining portion 8 f and the pattern recognizing portion 8 g determine the cause of an image defect corresponding to the specific part in the single-color test image, from one of the plurality of image creating portions 4 x that corresponds to the color of the single-color test image.
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Abstract
Description
[Math 2]
Pi=XiZi (2)
[Math 3]
Qi=Yi(−Zi) (3)
[Math 4]
Ri=Xi(1−Zi) (4)
[Math 5]
Ri=Yi(Zi−1) (5)
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US6553191B1 (en) * | 2000-09-11 | 2003-04-22 | Toshiba Tec Kabushiki Kaisha | Adjustment-control system for image forming apparatus |
JP2017083544A (en) | 2015-10-23 | 2017-05-18 | キヤノン株式会社 | Image processing device, image processing method, and program |
US20200051231A1 (en) * | 2018-08-13 | 2020-02-13 | Konica Minolta, Inc. | Image inspection device, image forming device, and computer-readable recording medium storing a program |
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JP2017083544A (en) | 2015-10-23 | 2017-05-18 | キヤノン株式会社 | Image processing device, image processing method, and program |
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